Fraud detection without the disks

Fraud detection without the disks

Telecom fraud is becoming increasingly sophisticated as networks move inexorably toward packet-based technology and the commercial telecommunications value chain become more complex, leading to a more varied threat landscape. The view is not all bleak, however, as robust data management and detection techniques are evolving to protect operators against financial losses related to fraud.

With the growth of service delivery in real-time, fraud identification and action must be faster than ever before. Recently, approaches to accelerating data analysis and implementing counter-action have received a boost from the introduction of a technology called in-memory computing. In-memory computing promises benefits including reduced system vulnerability caused by poor rule provisioning and more efficient use of hardware that helps ensure system scalability and improved accuracy.

Essentially, in-memory computing places the query data used by reporting tools within the RAM of a fraud system, rather than having it on disk as with the traditional approach. With in-memory techniques the information is first loaded into memory on the manager’s workstation. The fraud analyst can then query and interact with the data already loaded into his or her workstation’s own memory. Unlike caching techniques, where the available data is only a portion of the total, In-memory computing ensures that data available for analysis is as complete as possible.

The most obvious advantage of in-memory computing, and more specifically in-memory analysis, is the speed of analysis it enables. In addition to near real-time analytics, in-memory techniques can also enable predictive analysis with equally fast responses.

A fraud analyst could apply a data query using a defined formula or algorithm to help predict a potential fraud situation and receive not just the possible revenue loss outcome, but also information on how to respond to a particular case. With immediate results to queries and proactive pattern matching, this approach contrasts very favorably with the use of a disk-based tool for the same task. The latter approach can require recalculations or even database updates taking up valuable time, during which operators may be losing money to fraud.

Other options available in the fight against fraud include what might seem very obvious, which is to ensure that systems used for fraud management are not vulnerable to user error. Systems that are vulnerable to user error can fail because of poor rule provisioning, so controls should be in place to ensure the prevention of errors leading to bad rules, which themselves can impact on the entire system’s performance.